Have been learning about Kalman filtering in hopes of
providing my robot with a good sense of balance... In case
anyone else is interested in learning about Kalman
filtering, here are
the best links I've found so far. These are not the most
in-depth, but they are (far as I can tell) the ones with the
best "plain english" description of what all the formulas mean.
This one presents Kalman filtering of scalar values, rather
than vectors, which cuts down on the greek notation but
preserves the key concepts. The second link explains how
the scalar version is extended to work with vectors:
I found the links above to be the most helpful, but these
are also pretty good:
After a couple nights of reading these papers, the light
bulb suddenly went on... I get it! At least, I think I do.
I'll find out soon, as I just ordered a SparkFun 6DOF IMU, so I
should soon have some signals to filter.
I have to plug
Sparkfun here because this product appeared on their web
site just a couple months after I proposed the idea in their
online forum. I beg, they build, I buy. I wish more
companies worked that way. :-)